Predicting triaxial compressive strength of high-temperature treated rock using machine learning techniques

Hu, Xunjian; Shentu, Junjie; Xie, Ni; Huang, Yujie; Lei, Gang; Hu, Hai Feng; Guo, Panpan; Gong, Xiaonan

doi:10.1016/j.jrmge.2022.10.014

Cited by 24 publications

(4 citation statements)

References 61 publications

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“…In this research, the peak ground acceleration (PGA) has been used as a measure of earthquake intensity. For future research, using advances in machine learning [31,32,45,[60][61][62][63][64] can enable the extraction of effective earthquake features and the creation of PSDM curves based on these features.…”

Section: Limitations and Future Research Needsmentioning

confidence: 99%

Exploring the Effect of Near-Field Ground Motions on the Fragility Curves of Multi-Span Simply Supported Concrete Girder Bridges

Soltanmohammadi,

Mashayekhi,

Memarpour

et al. 2024

Infrastructures

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Investigating the impact of near-field ground motions on the fragility curves of multi-span simply supported concrete girder bridges is the main goal of this paper. Fragility curves are valuable tools for evaluating seismic risks and vulnerabilities of bridges. Numerous studies have investigated the impact of ground motions on the fragility curves of bridges. Ground motions are commonly categorized into two sets, based on the distance of the recorded station from the seismic source: far-field and near-field. Studies examining the influence of near-field records on bridge fragility curves vary depending on the specific bridge type and type of fragility curve being analyzed. Due to the widespread use of multi-span simply supported concrete girder bridges in the Central and Southeastern United States, this study makes use of this bridge type. This research investigates the component fragility curves for column curvatures, bearing deformations, and abutment displacements by employing 3-D analytical models and conducting nonlinear time history analysis. These curves illustrate the impact of near-field ground motions on different components. The component fragility curves for two sets of records, 91 near-field ground motions and 78 far-field ground motions, were obtained and compared. These findings demonstrate that near-field ground motions have a greater damaging effect on columns and abutments than far-field earthquakes. When it comes to bearing deformations, the far-field earthquake impact is more severe at lower intensities, whereas the impact of the near-field ground motion is stronger at higher intensities.

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Section: Limitations and Future Research Needsmentioning

confidence: 99%

Exploring the Effect of Near-Field Ground Motions on the Fragility Curves of Multi-Span Simply Supported Concrete Girder Bridges

Soltanmohammadi,

Mashayekhi,

Memarpour

et al. 2024

Infrastructures

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“…Though most of the reported studies on the mechanical properties of UHPC were experimental, it is imperative to predict this innovative material's properties using numerical and artificial intelligence approaches to assist in reaching an optimum mix design that fits the needs of a particular project. Recent studies attempted to use machine learning to predict the compressive strength of UHPC [23][24][25][26][27][28]. Khan et al [29] compared the capability of several machine learning models on predicting the compressive strength of UHPC with 14 input parameters.…”

Section: Introductionmentioning

confidence: 99%

Using Multiple Machine Learning Models to Predict the Strength of UHPC Mixes with Various FA Percentages

Safieh,

Hawileh,

Assad

et al. 2024

Infrastructures

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Ultra High-Performance Concrete (UHPC) has shown extraordinary performance in terms of strength and durability. However, having a cost-effective and sustainable UHPC mix design is a challenge in the construction sector. This study aims on building a predictable model that can help in determining the compressive strength of UHPC. The research focuses on applying multiple machine learning (ML) models and evaluating their performance in predicting the strength prediction of UHPC. Two reliable metrics are used to evaluate the performance of the model which are the coefficient of determination (R2) and mean squared error (MSE). The parameters that are affecting the compressive strength of UHPC are fly ash percentage levels (FA%), superplasticizer content, water to binder ratio (w/b), and curing period. A total of 54 ML models were used, consisting of Linear Regression, Support Vector Machines (SVM), Neural Networks, and Random forests algorithms. Among these models, Random Forest proved to be the most effective in capturing the relationships in UHPC’s behaviour with an R squared score of 0.8857. The Random Forest ML model is also used in this paper to conduct a parametric study that will help in obtaining the compressive strength of UHPC with higher content of FA%, which is not sufficiently studied in the literature.

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“…Damage to a bridge's construction that results from explosions or natural catastrophes, such as earthquakes, affects the material's characteristics, boundary conditions, and structural integrity [7]. Structural problems are also a result of service load factors, including aging, traffic expansion, increasing degradation, and environmental effects [8,9]. The benefit of putting in place a bridge health monitoring system is that it allows for an objective evaluation of the structure's state over time.…”

Section: Introductionmentioning

confidence: 99%

Bridge Monitoring Strategies for Sustainable Development with Microwave Radar Interferometry

Zou,

Feng,

Masci

et al. 2024

Sustainability

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The potential of a coherent microwave radar for infrastructure health monitoring has been investigated over the past decade. Microwave radar measuring based on interferometry processing is a non-invasive technique that can measure the line-of-sight (LOS) displacements of large infrastructure with sub-millimeter precision and provide the corresponding frequency spectrum. It has the capability to estimate infrastructure vibration simultaneously and remotely with high accuracy and repeatability, which serves the long-term serviceability of bridge structures within the context of the long-term sustainability of civil engineering infrastructure management. In this paper, we present three types of microwave radar systems employed to monitor the displacement of bridges in Japan and Italy. A technique that fuses polarimetric analysis and the interferometry technique for bridge monitoring is proposed. Monitoring results achieved with full polarimetric real aperture radar (RAR), step-frequency continuous-wave (SFCW)-based linear synthetic aperture, and multi-input multi-output (MIMO) array sensors are also presented. The results reveal bridge dynamic responses under different loading conditions, including wind, vehicular traffic, and passing trains, and show that microwave sensor interferometry can be utilized to monitor the dynamics of bridge structures with unprecedented spatial and temporal resolution. This paper demonstrates that microwave sensor interferometry with efficient, cost-effective, and non-destructive properties is a serious contender to employment as a sustainable infrastructure monitoring technology serving the sustainable development agenda.

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Predicting triaxial compressive strength of high-temperature treated rock using machine learning techniques

Cited by 24 publications

References 61 publications

Exploring the Effect of Near-Field Ground Motions on the Fragility Curves of Multi-Span Simply Supported Concrete Girder Bridges

Exploring the Effect of Near-Field Ground Motions on the Fragility Curves of Multi-Span Simply Supported Concrete Girder Bridges

Using Multiple Machine Learning Models to Predict the Strength of UHPC Mixes with Various FA Percentages

Bridge Monitoring Strategies for Sustainable Development with Microwave Radar Interferometry

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